Gas storage facilities have long faced bottlenecks in the core control systems of high-power compressors, where operational safety, efficiency, and automation under complex conditions remain key challenges. Traditional single-sensor monitoring offers limited perception and low data utilization, falling short of ensuring reliable operation and intelligent decision-making. With AI engineering advancing toward multi modal and heterogeneous industrial applications, research has increasingly focused on multi-sensor fusion and intelligent diagnostics. Although multi-sensor networks provide complementary information and enhanced perception, issues like data heterogeneity, class imbalance, and cross-domain distribution differences continue to constrain diagnostic performance. To address these issues, a Dual-branch Heterogeneous Synergistic Network (DHSNet) is proposed to achieve cross-modal fusion of vibration signals and infrared thermal imaging signals. The framework incorporates a cross domain adaptation strategy to enhance domain-invariant feature learning and a dynamic focal loss function to adaptively adjust class weights based on real-time output indicators, mitigating the impact of sample imbalance. Experimental results demonstrate that the proposed method achieves an average diagnostic accuracy of 93.27% in cross-load transfer tasks, outperforming other methods used in imbalanced scenarios by over 4%. Besides, the proposed method maintains robust diagnostic performance exceeding 90% accuracy across most load transfer scenarios, even under moderately imbalanced data conditions. Feature contribution analysis further validates the effectiveness of multi modal synergy, and revealing the complementary mechanism of multi-source data. This study enhances Prognostics and Health Management (PHM) systems' engineering applicability and perception capabilities in multi-sensor industrial environments, providing reliable multi-modal diagnostics to advance intelligent maintenance.

In industrial scenarios, mechanical faults are episodic and uncertain. Thus the monitoring data collected is usually extremely imbalanced, resulting in intelligent diagnostic models that suffer from majority-class dominance, minority-class overfitting, and poor generalization performance. Therefore, a knowledge distillation-guided cost-sensitive ensemble learning framework is proposed. It effectively combines ensemble learning and cost-sensitive learning to fully extract the multiscale features, effectively leverage the critical multi-depth features, and emphasize classifying the most confusing classes. Specifically, multiple-scale feature extraction and multi-order fusion are first employed to fully utilize the fault information. Afterward, the complementary diagnostic knowledge at different depths of the network is embedded into a novel ensemble learning process for better integration decisions. Then an improved knowledge distillation method achieves the mutual transfer and sublimation of excellent diagnostic knowledge while focusing on the most confusing fault classes to achieve the effective representation of various types of faults. Finally, a cost-sensitive strategy is applied to further increase attention to minority classes. The experimental results for various complex data imbalance scenarios, including extreme imbalance, step imbalance, continuous imbalance, interclass imbalance, and intra-class imbalance, all indicate that the proposed method can achieve state-of-the-art performance and provide a promising solution for the practical industrial application of intelligent diagnostic methods.